Derivation and validation of a simple perioperative sleep apnea prediction score

  • Ramachandran S
  • Kheterpal S
  • Consens F
 et al. 
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Abstract

BACKGROUND: Obstructive sleep apnea (OSA) is a largely underdiagnosed, common condition, which is important to diagnose preoperatively because it has implications for perioperative management. Our purpose in this study was to identify independent clinical predictors of a diagnosis of OSA in a general surgical population, develop a perioperative sleep apnea prediction (P-SAP) score based on these variables, and validate the P-SAP score against standard overnight polysomnography. METHODS: A retrospective, observational study was designed to identify patients with a known diagnosis of OSA. Independent predictors of a diagnosis of OSA were derived by logistic regression, based on which prediction tool (P-SAP score) was developed. The P-SAP score was then validated in patients undergoing overnight polysomnography. RESULTS: The P-SAP score was derived from 43,576 adult cases undergoing anesthesia. Of these, 3884 patients (7.17%) had a documented diagnosis of OSA. Three demographic variables: age > 43 years, male gender, and obesity; 3 history variables: history of snoring, diabetes mellitus Type 2, and hypertension; and 3 airway measures: thick neck, modified Mallampati class 3 or 4, and reduced thyromental distance were identified as independent predictors of a diagnosis of OSA. A diagnostic threshold P-SAP score > or = 2 showed excellent sensitivity (0.939) but poor specificity (0.323), whereas for a P-SAP score > or = 6, sensitivity was poor (0.239) with excellent specificity (0.911). Validation of this P-SAP score was performed in 512 patients with similar accuracy. CONCLUSION: The P-SAP score predicts diagnosis of OSA with dependable accuracy across mild to severe disease. The elements of the P-SAP score are derived from a typical university hospital surgical population.

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Authors

  • Satya Krishna Ramachandran

  • Sachin Kheterpal

  • Flavia Consens

  • Amy Shanks

  • Tara M. Doherty

  • Michelle Morris

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